Statement of the Technical Field
The invention concerns transducer systems. More particularly, the invention concerns transducer systems and methods for matching gain levels of the transducer systems.
Description of the Related Art
There are various conventional systems that employ transducers. Such systems include, but are not limited to, communication systems and hearing aid systems. These systems often employ various noise cancellation techniques to reduce or eliminate unwanted sound from audio signals received at one or more transducers (e.g., microphones).
One conventional noise cancellation technique uses a plurality of microphones to improve speech quality of an audio signal. For example, one such conventional multi-microphone noise cancellation technique is described in the following document: B. Widrow, R. C. Goodlin, et al., Adaptive Noise Cancelling: Principles and Applications, Proceedings of the IEEE, vol. 63, pp. 1692-1716, December 1975. This conventional multi-microphone noise cancellation technique uses two (2) microphones to improve speech quality of an audio signal. A first one of the microphones receives a “primary” input containing a corrupted signal. A second one of the microphones receives a “reference” input containing noise correlated in some unknown way to the noise of the corrupted signal. The “reference” input is adaptively filtered and subtracted from the “primary” input to obtain a signal estimate.
In the above-described multi-microphone noise cancellation technique, the noise cancellation performance depends on the degree of match between the two microphone systems. The balance of the gain levels between the microphone systems is important to be able to effectively remove far field noise from an input signal. For example, if the gain levels of the microphone systems are not matched, then the amplitude of a signal received at the first microphone system will be amplified by a larger amount as compared to the amplitude of a signal received at the second microphone system. In this scenario, a signal resulting from the subtraction of the signals received at the two microphone systems will contain some unwanted far field noise. In contrast, if the gain levels of the microphone systems are matched, then the amplitudes of the signals received at the microphone systems are amplified by the same amount. In this scenario, a signal resulting from the subtraction of signals received at the microphone systems is absent of far field noise.
The following table illustrates how well balanced the gain levels of the microphone systems have to be to effectively remove far field noise from a received signal.
Microphone Difference (dB)Noise Suppression (dB)1.0019.192.0013.693.0010.664.008.635.007.166.006.02For typical users, a reasonable noise rejection performance is nineteen to twenty decibels (19 dB to 20 dB) of noise rejection. In order to achieve the minimum acceptable noise rejection, microphone systems are needed with gain tolerances better than +/−0.5 dB, as shown in the above provided table. Also, the response of the microphones must also be within this tolerance across the frequency range of interest (e.g., 300 Hz to 3500 Hz) for voice. The response of the microphones can be affected by acoustic factors, such as port design which may be different between the two microphones. In this scenario, the microphone systems need to have a difference in gain levels equal to or less than 1 dB. Such microphones are not commercially available. However, microphones with gain tolerances of +/−1 dB and +/−3 dB do exist. Since the microphones with gain tolerances of +/−3 dB are less expensive and more available as compared to the microphones with gain tolerances of +/−1 dB, they are typically used in the systems employing the multi-microphone noise cancellation techniques. In these conventional systems, a noise rejection better than 6 dB cannot be guaranteed as shown in the above provided table. Therefore, a plurality of solutions have been derived for providing a noise rejection better than 6 dB in systems employing conventional microphones.
A first solution involves utilizing tighter tolerance microphones, e.g., microphones with gain tolerances of +/−1 dB. In this scenario, the amount of noise rejection is improved from 6 dB to approximately 14 dB, as shown by the above provided table. Although the noise rejection is improved, this first solution suffers from certain drawbacks. For example, the tighter tolerance microphones are more expensive as suggested above, and long term drift can, over time, cause performance degradation.
A second solution involves calibrating the microphone systems at the factory. The calibration process involves: manually adjusting a sensitivity of the microphone systems such that they meet the +/−0.5 dB gain difference specification; and storing the gain adjustment values in the device. This second solution suffers from certain drawbacks. For example, the cost of manufacture is relatively high as a result of the calibration process. Also, there is an inability to compensate for drifts and changes in system characteristics which occur overtime.
A third solution involves performing a Least Means Squares (LMS) based solution or a time domain solution. The LMS based solution involves adjusting taps on a Finite Impulse Response (FIR) filter until a minimum output occurs. The minimum output indicates that the gain levels of the microphone systems are balanced. This third solution suffers from certain drawbacks. For example, this solution is computationally intensive. Also, the time it takes to acquire a minimum output can be undesirably long.
A fourth solution involves performing a trimming algorithm based solution. The trimming algorithm based solution is similar to the factory calibration solution described above. The difference between these two solutions is who performs the calibration of the transducers. In the factory calibration solution, an operator at the factory performs said calibration. In the trimming algorithm based solution, the user performs said calibration. One can appreciate that the trimming algorithm based solution is undesirable since the burden of calibration is placed on the user and the quality of the results are likely to vary.